- Collection and acquiring data from different sources (web crawling and scraping)
- Data tidying and cleaning, Data transformation, summarization and aggregation, organizing data and preparing for analysis
- EDA and data visualization techniques - create evidence-based research and make correct, unbiased conclusions
- Working with structured (tabular) and unstructured data (images and text)
- Basics of statistical models / machine learning (Linear and Logistic regression)
- Building a complete project - best practices